From 2a2c15723b7cab124bd7f8d9a2b4d3b11dbf126e Mon Sep 17 00:00:00 2001
From: SangBin Cho <rkooo567@gmail.com>
Date: Wed, 6 May 2020 10:24:51 -0700
Subject: [PATCH] Revert "Async actor microbenchmark Script (#8275)"

This reverts commit 6a6eead1fe45c774ce75da0d5f90f443ac3748ec.
---
 python/ray/ray_perf.py | 58 ------------------------------------------
 1 file changed, 58 deletions(-)

diff --git a/python/ray/ray_perf.py b/python/ray/ray_perf.py
index eae62e396..bcda18792 100644
--- a/python/ray/ray_perf.py
+++ b/python/ray/ray_perf.py
@@ -1,6 +1,5 @@
 """This is the script for `ray microbenchmark`."""
 
-import asyncio
 import os
 import time
 import numpy as np
@@ -23,18 +22,6 @@ class Actor:
         ray.get([small_value.remote() for _ in range(n)])
 
 
-@ray.remote
-class AsyncActor:
-    async def small_value(self):
-        return b"ok"
-
-    async def small_value_with_arg(self, x):
-        return b"ok"
-
-    async def small_value_batch(self, n):
-        await asyncio.wait([small_value.remote() for _ in range(n)])
-
-
 @ray.remote(num_cpus=0)
 class Client:
     def __init__(self, servers):
@@ -203,51 +190,6 @@ def main():
     timeit("n:n actor calls with arg async", actor_multi2_direct_arg,
            n * len(clients))
 
-    a = AsyncActor.remote()
-
-    def actor_sync():
-        ray.get(a.small_value.remote())
-
-    timeit("1:1 async-actor calls sync", actor_sync)
-
-    a = AsyncActor.remote()
-
-    def async_actor():
-        ray.get([a.small_value.remote() for _ in range(1000)])
-
-    timeit("1:1 async-actor calls async", async_actor, 1000)
-
-    a = AsyncActor.remote()
-
-    def async_actor():
-        ray.get([a.small_value_with_arg.remote(i) for i in range(1000)])
-
-    timeit("1:1 async-actor calls with args async", async_actor, 1000)
-
-    n = 5000
-    n_cpu = multiprocessing.cpu_count() // 2
-    actors = [AsyncActor.remote() for _ in range(n_cpu)]
-    client = Client.remote(actors)
-
-    def async_actor_async():
-        ray.get(client.small_value_batch.remote(n))
-
-    timeit("1:n async-actor calls async", async_actor_async, n * len(actors))
-
-    n = 5000
-    m = 4
-    n_cpu = multiprocessing.cpu_count() // 2
-    a = [AsyncActor.remote() for _ in range(n_cpu)]
-
-    @ray.remote
-    def async_actor_work(actors):
-        ray.get([actors[i % n_cpu].small_value.remote() for i in range(n)])
-
-    def async_actor_multi():
-        ray.get([async_actor_work.remote(a) for _ in range(m)])
-
-    timeit("n:n async-actor calls async", async_actor_multi, m * n)
-
 
 if __name__ == "__main__":
     main()